Use of primary care data for identifying individuals at risk of cardiovascular disease

The aim of this research was to explore the potential of routinely collected primary care data to support the identification of individuals for cardiovascular risk reduction. The work involved a systematic literature review of reminder interventions operating at the point of care; a randomised controlled trial of a novel software tool to facilitate the targeting of individuals at risk of cardiovascular disease; and an exploration of qualitative issues relevant to the challenge of cardiovascular risk reduction in current practice. The Systematic review resulted in a narrative synthesis and a meta-analysis. It concluded that reminder interventions are generally effective at changing practitioner behaviour, but the effect is inconsistent, probably dependent on organisational context, and difficult to predict. The e-Nudge trial involved 19 practices in Coventry and Warwickshire, who used the e-Nudge software tool for two years. This tool was programmed for the project by the clinical software company EMIS. Whilst the primary outcome (cardiovascular event rate) was not significantly reduced in this timescale, a beneficial effect was demonstrated on the adequacy of data to support risk estimation and on the visibility of the at risk population. A new means of addressing the problem of undiagnosed and late-diagnosed diabetes was also discovered. Qualitative aspects of this area of care are presented through a discussion of ethical issues, a limited series of interviews with members of the public included in the appendix, and extensive field notes taken throughout the research. These provide some context in support of the e-Nudge trial. Routinely collected data of UK general practice provide a potentially rich resource to support primary cardiovascular disease prevention, but practical, ethical and conceptual issues must all be addressed to optimise their impact. This conclusion forms the thesis to be explored and justified through this dissertation

A data driven nonlinear stochastic model for blood glucose dynamics

The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose–insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose–insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles

Use of primary care data for identifying individuals at risk of cardiovascular disease

The aim of this research was to explore the potential of routinely collected primary care data to support the identification of individuals for cardiovascular risk reduction. The work involved a systematic literature review of reminder interventions operating at the point of care; a randomised controlled trial of a novel software tool to facilitate the targeting of individuals at risk of cardiovascular disease; and an exploration of qualitative issues relevant to the challenge of cardiovascular risk reduction in current practice. The Systematic review resulted in a narrative synthesis and a meta-analysis. It concluded that reminder interventions are generally effective at changing practitioner behaviour, but the effect is inconsistent, probably dependent on organisational context, and difficult to predict. The e-Nudge trial involved 19 practices in Coventry and Warwickshire, who used the e-Nudge software tool for two years. This tool was programmed for the project by the clinical software company EMIS. Whilst the primary outcome (cardiovascular event rate) was not significantly reduced in this timescale, a beneficial effect was demonstrated on the adequacy of data to support risk estimation and on the visibility of the at risk population. A new means of addressing the problem of undiagnosed and late-diagnosed diabetes was also discovered. Qualitative aspects of this area of care are presented through a discussion of ethical issues, a limited series of interviews with members of the public included in the appendix, and extensive field notes taken throughout the research. These provide some context in support of the e-Nudge trial. Routinely collected data of UK general practice provide a potentially rich resource to support primary cardiovascular disease prevention, but practical, ethical and conceptual issues must all be addressed to optimise their impact. This conclusion forms the thesis to be explored and justified through this dissertation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development and validation of the DIabetes Severity SCOre (DISSCO) in 139 626 individuals with type 2 diabetes: a retrospective cohort study

OBJECTIVE: Clinically applicable diabetes severity measures are lacking, with no previous studies comparing their predictive value with glycated hemoglobin (HbA1c). We developed and validated a type 2 diabetes severity score (the DIabetes Severity SCOre, DISSCO) and evaluated its association with risks of hospitalization and mortality, assessing its additional risk information to sociodemographic factors and HbA1c. RESEARCH DESIGN AND METHODS: We used UK primary and secondary care data for 139 626 individuals with type 2 diabetes between 2007 and 2017, aged ≥35 years, and registered in general practices in England. The study cohort was randomly divided into a training cohort (n=111 748, 80%) to develop the severity tool and a validation cohort (n=27 878). We developed baseline and longitudinal severity scores using 34 diabetes-related domains. Cox regression models (adjusted for age, gender, ethnicity, deprivation, and HbA1c) were used for primary (all-cause mortality) and secondary (hospitalization due to any cause, diabetes, hypoglycemia, or cardiovascular disease or procedures) outcomes. Likelihood ratio (LR) tests were fitted to assess the significance of adding DISSCO to the sociodemographics and HbA1c models. RESULTS: A total of 139 626 patients registered in 400 general practices, aged 63±12 years were included, 45% of whom were women, 83% were White, and 18% were from deprived areas. The mean baseline severity score was 1.3±2.0. Overall, 27 362 (20%) people died and 99 951 (72%) had ≥1 hospitalization. In the training cohort, a one-unit increase in baseline DISSCO was associated with higher hazard of mortality (HR: 1.14, 95% CI 1.13 to 1.15, area under the receiver operating characteristics curve (AUROC)=0.76) and cardiovascular hospitalization (HR: 1.45, 95% CI 1.43 to 1.46, AUROC=0.73). The LR tests showed that adding DISSCO to sociodemographic variables significantly improved the predictive value of survival models, outperforming the added value of HbA1c for all outcomes. Findings were consistent in the validation cohort. CONCLUSIONS: Higher levels of DISSCO are associated with higher risks for hospital admissions and mortality. The new severity score had higher predictive value than the proxy used in clinical practice, HbA1c. This reproducible algorithm can help practitioners stratify clinical care of patients with type 2 diabetes

Trends in the full blood count blood test and colorectal cancer detection: a longitudinal, case-control study of UK primary care patient data [version 2; peer review: 2 approved, 1 not approved]

Background: The full blood count (FBC) is a common blood test performed in general practice. It consists of many individual parameters that may change over time due to colorectal cancer. Such changes are likely missed in practice. We identified trends in these FBC parameters to facilitate early detection of colorectal cancer. Methods: We performed a retrospective, case-control, longitudinal analysis of UK primary care patient data. LOWESS smoothing and mixed effects models were derived to compare trends in each FBC parameter between patients diagnosed and not diagnosed over a prior 10-year period. Results: There were 399,405 males (2.3%, n = 9,255 diagnosed) and 540,544 females (1.5%, n = 8,153 diagnosed) in the study. There was no difference between cases and controls in FBC trends between 10 and four years before diagnosis. Within four years of diagnosis, trends in many FBC levels statistically significantly differed between cases and controls, including red blood cell count, haemoglobin, white blood cell count, and platelets (interaction between time and colorectal cancer presence: p <0.05). FBC trends were similar between Duke’s Stage A and D colorectal tumours, but started around one year earlier in Stage D diagnoses. Conclusions: Trends in FBC parameters are different between patients with and without colorectal cancer for up to four years prior to diagnosis. Such trends could help earlier identification

Measuring the complexity of general practice consultations:development and validation of a complexity measure

Background: The complexity of general practice consultations may be increasing and varies in different settings. A measure of complexity is required to test these hypotheses. Aim: To develop a valid measure of general practice consultation complexity applicable to routine medical records. Design and setting: Delphi study to select potential indicators of complexity followed by a cross-sectional study in English general practices to develop and validate a complexity measure. Method: The online Delphi study over two rounds identified potential indicators of consultation complexity. The cross-sectional study used an age–sex stratified random sample of patients and general practice face-to-face consultations from 2013/2014 in the Clinical Practice Research Datalink. The authors explored independent relationships between each indicator and consultation duration using mixed-effects regression models, and revalidated findings using data from 2017/2018. The proportion of complex consultations in different age–sex groups was assessed. Results: A total of 32 GPs participated in the Delphi study. The Delphi panel endorsed 34 of 45 possible complexity indicators after two rounds. After excluding factors because of low prevalence or confounding, 17 indicators were retained in the cross-sectional study. The study used data from 173 130 patients and 725 616 face-to-face GP consultations. On defining complexity as the presence of any of these 17 factors, 308 370 consultations (42.5%) were found to be complex. Mean duration of complex consultations was 10.49 minutes, compared to 9.64 minutes for non-complex consultations. The proportion of complex consultations was similar in males and females but increased with age. Conclusion: The present consultation complexity measure has face and construct validity. It may be useful for research, management and policy, and for informing decisions about the range of resources needed in different practices

Protocol for the 'e-Nudge trial' : a randomised controlled trial of electronic feedback to reduce the cardiovascular risk of individuals in general practice [ISRCTN64828380]

Background: Cardiovascular disease (including coronary heart disease and stroke) is a major cause of death and disability in the United Kingdom, and is to a large extent preventable, by lifestyle modification and drug therapy. The recent standardisation of electronic codes for cardiovascular risk variables through the United Kingdom's new General Practice contract provides an opportunity for the application of risk algorithms to identify high risk individuals. This randomised controlled trial will test the benefits of an automated system of alert messages and practice searches to identify those at highest risk of cardiovascular disease in primary care databases. Design: Patients over 50 years old in practice databases will be randomised to the intervention group that will receive the alert messages and searches, and a control group who will continue to receive usual care. In addition to those at high estimated risk, potentially high risk patients will be identified who have insufficient data to allow a risk estimate to be made. Further groups identified will be those with possible undiagnosed diabetes, based either on elevated past recorded blood glucose measurements, or an absence of recent blood glucose measurement in those with established cardiovascular disease. Outcome measures: The intervention will be applied for two years, and outcome data will be collected for a further year. The primary outcome measure will be the annual rate of cardiovascular events in the intervention and control arms of the study. Secondary measures include the proportion of patients at high estimated cardiovascular risk, the proportion of patients with missing data for a risk estimate, and the proportion with undefined diabetes status at the end of the trial

Factors associated with consultation rates in general practice in England, 2013-2014:a cross-sectional study

Background Workload in general practice has risen during the last decade, but the factors associated with this increase are unclear. Aim To examine factors associated with consultation rates in general practice. Design and setting A cross-sectional study. A sample of 304,937 patients registered at 316 English practices between 2013 and 2014 was drawn from the Clinical Practice Research Datalink. Method We linked age, sex, ethnicity, smoking status, and deprivation measures with practice level data on staffing, rurality, training practice status, and Quality and Outcomes Framework performance. We conducted multilevel analyses of patient consultation rates. Results Consultations were grouped into three types: General practitioner or nurse (All), general practitioner (GP), and nurse. Non-smokers consulted less than current smokers (All: RR=0.88, 95% CI: 0.87 to 0.89; GP: 0.88 [0.87 to 0.89]; nurse: 0.91 [0.90 to 0.92]. Consultation rates were higher for those in the most deprived quintile compared to the least deprived quintile (All: 1.18 [1.16 to 1.19]; GP: 1.17 [1.15 to 1.19]; nurse: 1.13 [1.11 to 1.15]. For all three consultation types, consultation rates increased with age, female sex, and varied by ethnicity. Rates in practices with between >8 and Conclusions Our analyses show consistent trends in factors related to consultation rates in general practice across three types of consultation. These data can be used inform the development of more sophisticated staffing models, and resource allocation formulae.</p

Patient-level and practice-level factors associated with consultation duration:a cross-sectional analysis of over one million consultations in English primary care

Objectives: Consultation duration has previously been shown to be associated with patient, practitioner, and practice characteristics. However, previous studies were conducted outside the UK, considered only small numbers of GP consultations, or focused primarily on practitioner level characteristics. We aimed to determine the patient and practice level factors associated with duration of GP and nurse consultations in UK primary care. Design and setting: Cross sectional data were obtained from English general practices contributing to the Clinical Practice Research Datalink (CPRD) linked to data on patient deprivation and practice staffing, rurality, and Quality and Outcomes Framework (QOF) achievement. Participants: 218,304 patients, from 316 English general practices, consulting from 1st April 2013 to 31st March 2014. Analysis: Multilevel mixed effects models described the association between consultation duration and patient and practice-level factors (patient age, gender, smoking status, ethnic group, deprivation and practice rurality, number of full time equivalent GPs/nurses, list size, consultation rate, quintile of overall QOF achievement, and training status). Results: Mean duration of face-to-face GP consultations was 9.24 minutes and 5.32 minutes for telephone consultations. Nurse face-to-face and telephone consultations lasted 9.70 and 5.73 minutes on average, respectively. Longer GP consultation duration was associated with female patient gender, practice training status and older patient age. Shorter duration was associated with higher deprivation and consultation rate. Longer nurse consultation duration was associated with male patient gender, older patient age and ever smoking; and shorter duration with higher consultation rate. Observed differences in duration were small (e.g. GP consultations with female patients compared to male patients were 8 seconds longer on average). Conclusions: Small observed differences in consultation duration indicate that patients are treated similarly regardless of background. Increased consultation duration may be beneficial for older or comorbid patients, but the benefits and costs of increased consultation duration require further study.</p